Computer vision technologies that rely on deep learning, such as computer vision technologies based on convolutional neural networks (CNNs), can accomplish complex tasks in a reliable and robust manner. For example, the automotive industry deploys advanced computer vision chipsets in autonomous vehicles and in safety features, such as obstacle detection and collision avoidance systems in automobiles. In the manufacturing and warehousing sectors, neural network and deep learning techniques are being implemented to develop adaptable robots that perform human-like tasks. In security and surveillance applications, embedded devices with neural network and deep learning capabilities conduct real-time image analyses from vast amounts of data. In mobile and entertainment devices, deep learning enables ‘intelligent’ image and video capture and searches, as well as delivery of virtual reality-based content.
A barrier to the widespread adoption of neural network and deep learning in embedded devices is the extremely high computation cost of neural network and deep learning algorithms. Some computer vision products use programmable general purpose graphics processing units (GPUs). These chips can be power-consumptive while battery-operated embedded devices can be designed for low power, efficient operation. Even devices that are not battery-operated, e.g., devices that can be plugged into a wall outlet and power over Ethernet (POE) device (such as a home security camera system), may be designed for low power, efficient operation, for example, because of thermal management requirement (such as the amount of heat dissipation a device can have). Some computer vision products use specialized chips that rely on fixed function accelerators, which lack flexibility and programmability even though not necessarily power consumptive.